Introduction to multivariate analysis: linear and nonlinear modeling

✍ Scribed by Konishi, Sadanori

• Publisher: Chapman & Hall/CRC
• Year: 2014
• Tongue: English
• Leaves: 336
• Series: Chapman & Hall/CRC texts in statistical science series
• Category: Library

✦ Synopsis

""The presentation is always clear and several examples and figures facilitate an easy understanding of all the techniques. The book can be used as a textbook in advanced undergraduate courses in multivariate analysis, and can represent a valuable reference manual for biologists and engineers working with multivariate datasets.""--Fabio Rapallo, Zentralblatt MATH 1296.;Front Cover; Contents; List of Figures; List of Tables; Preface; 1. Introduction; 2. Linear Regression Models; 3. Nonlinear Regression Models; 4. Logistic Regression Models; 5. Model Evaluation and Selection; 6. Discriminant Analysis; 7. Bayesian Classification; 8. Support Vector Machines; 9. Principal Component Analysis; 10. Clustering; A. Bootstrap Methods; B. Lagrange Multipliers; C. EM Algorithm; Bibliography.

✦ Table of Contents

Front Cover......Page 1
Contents......Page 8
List of Figures......Page 14
List of Tables......Page 22
Preface......Page 24
1. Introduction......Page 28
2. Linear Regression Models......Page 42
3. Nonlinear Regression Models......Page 82
4. Logistic Regression Models......Page 114
5. Model Evaluation and Selection......Page 132
6. Discriminant Analysis......Page 164
7. Bayesian Classification......Page 200
8. Support Vector Machines......Page 220
9. Principal Component Analysis......Page 252
10. Clustering......Page 286
A. Bootstrap Methods......Page 310
B. Lagrange Multipliers......Page 314
C. EM Algorithm......Page 320
Bibliography......Page 326

✦ Subjects

Multivariate analysis;Electronic books